2017
EACL
EACL 2017
Continuous N-gram Representations for Authorship Attribution
Abstract
AbstractThis paper presents work on using continuous representations for authorship attribution. In contrast to previous work, which uses discrete feature representations, our model learns continuous representations for n-gram features via a neural network jointly with the classification layer. Experimental results demonstrate that the proposed model outperforms the state-of-the-art on two datasets, while producing comparable results on the remaining two.
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Interdisciplinary Bridge
— Deep Learning and Machine Learning and Natural Language Processing
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Keyword Pioneer
— continuous n-gram
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Hot Topic Early Bird
— authorship attribution
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio